code: def _binary_fbeta_score_arg_validation(
      beta: float,
      threshold: float = 0.5,
      multidim_average: Literal["global", "samplewise"] = "global",
      ignore_index: Optional[int] = None,
  ) -> None:
      if not (isinstance(beta, float) and beta > 0):
          raise ValueError(
              f"Expected argument `beta` to be a float larger than 0, but got {beta}."
          )
      _binary_stat_scores_arg_validation(
          threshold, multidim_average, ignore_index
      )
  
file_path: torchmetrics/src/torchmetrics/functional/classification/f_beta.py
imports: from typing import Optional
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.classification.stat_scores import _binary_stat_scores_arg_validation, _binary_stat_scores_format, _binary_stat_scores_tensor_validation, _binary_stat_scores_update, _multiclass_stat_scores_arg_validation, _multiclass_stat_scores_format, _multiclass_stat_scores_tensor_validation, _multiclass_stat_scores_update, _multilabel_stat_scores_arg_validation, _multilabel_stat_scores_format, _multilabel_stat_scores_tensor_validation, _multilabel_stat_scores_update
  from torchmetrics.utilities.compute import _adjust_weights_safe_divide, _safe_divide
  from torchmetrics.utilities.enums import ClassificationTask
label_desc: Use image node_id 2 for calling a global function with example usage: _binary_fbeta_score_arg_validation(beta, threshold, multidim_average, ignore_index) without return types